The human brain is made up of an array of functionally and structurally defined regions. Localizing these regions is critical for early diagnosis of an array of diseases such as Alzheimer's disease (AD), as well as for neuroscientific research aimed at understanding the brain's functional and structural properties and clinical intervention in drug-resistant depression. Unfortunately, direct visualization of the laminar properties that are one of the defining characteristics of cortical areas is beyond current imaging technology, except in specialized cases that represent a small fraction of the entire cortex. In this project we propose to develop tools for segmenting cortical areas and laminar boundaries using ultra-high resolution ex vivo MRI and optical coherence tomography (OCT), then to make in vivo inferences via probabilistic modeling. This will allow us to develop and distribute tools that should permit automated areal and laminar localization, facilitating an array of clinical and neuroscientific research. The proposed analysis stream holds the promise of automatically deriving new laminar and area-specific morphometric measures, a critical advance in our goal of detecting neurodegenerative disorders early in their course, when therapeutic intervention is still possible, as well as for guiding disease treatment and staging in individual patients suffering from diseases such as depression or multiple sclerosis.